Electronic storage mechanisms have enabled accumulation of massive amounts of data. For instance, data that previously required volumes of books for recordation can now be stored electronically without expense of printing paper and with a fraction of space needed for storage of paper. In one particular example, deeds and mortgages that were previously recorded in paper volumes can now be stored electronically. Moreover, advances in sensors and other electronic mechanisms now allow massive amounts of data to be collected in real-time. For instance, GPS systems can determine location of an individual or entity by way of satellites and GPS receivers. Electronic storage devices connected thereto can then be employed to retain locations associated with such systems. Various other sensors and data collection devices can also be utilized for obtainment and storage of data.
Collected data relating to particular contexts and/or applications can be employed in connection with data trending and analysis, and predictions can be made as a function of received and analyzed data. Such prediction is, in fact, human nature, and individuals frequently generate such predictions. For instance, a person traveling between a place of employment and a place of residence can determine that during certain times of day within weekdays traffic conditions are subject to high levels of congestion. Thus, prior to leaving a place of work, an individual can predict when and where one will most likely be slowed in traffic, and can further predict how long they will be subject to congestion. The individual's predictions can further be a function of other variables, such as weather, whether a day is a holiday, events that are geographically proximate, and the like. Thus, when an individual has access to contextual information and has access to (e.g., by way of memory) historical data, the individual can generate predictions.
Predictive models utilized on computer systems can often produce more accurate predictive results than a human, as computer systems may have access to a substantial amount of data. For instance, a computer application can have access to data that represents traffic patterns over twenty years, whereas an individual may have experienced traffic patterns for less than a year. These predictive models can be quite effective when generating predictions associated with common occurrences. Predictive models, however, can flood an individual with predictions and/or information, including superfluous information that the individual can readily predict without aid of a model. In one example, an individual is not aided when a predictive model informs the individual that traffic will be congested during rush hour within a large city, as the user can readily make such prediction accurately without utilization of the model.
Furthermore, predictive models can fail when associated with events that are atypical. Reasons for failure can include lack of understanding of a situation, lack of contemplation of a situation, infrequency of occurrence of an event, and a variety of other factors. Alerting an individual of a surprising event, however, is more critical than alerting the individual of a typical event, because (as alluded to above) such individual may very well have predicted the typical event without aid of a predictive application. Conventionally, however, there is no suitable mechanism for determining what amounts to an anomalous event and/or alerting the individual of occurrence of an anomalous event.